From e62dd415ae637f508f3c59c0111f779d302a5d4f Mon Sep 17 00:00:00 2001 From: Sefik Ilkin Serengil Date: Wed, 29 Apr 2020 09:47:40 +0300 Subject: [PATCH] autoencode --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a76ed93..aa370aa 100644 --- a/README.md +++ b/README.md @@ -70,7 +70,7 @@ DeepFace.verify("img1.jpg", "img2.jpg", model_name = "VGG-Face", model = model) ## Similarity -These models actually find the vector embeddings of faces. In other words, we use face recognition models as [autoencoders](https://sefiks.com/2018/03/23/convolutional-autoencoder-clustering-images-with-neural-networks/). Decision of verification is based on the distance between vectors. Distance could be found by different metrics such as [`Cosine Similarity`](https://sefiks.com/2018/08/13/cosine-similarity-in-machine-learning/), Euclidean Distance and L2 form. The default configuration finds the **cosine similarity**. You can alternatively set the similarity metric while verification as demostratred below. +These models actually find the vector embeddings of faces. In other words, we use face recognition models as [`autoencoders`](https://sefiks.com/2018/03/23/convolutional-autoencoder-clustering-images-with-neural-networks/). Decision of verification is based on the distance between vectors. Distance could be found by different metrics such as [`Cosine Similarity`](https://sefiks.com/2018/08/13/cosine-similarity-in-machine-learning/), Euclidean Distance and L2 form. The default configuration finds the **cosine similarity**. You can alternatively set the similarity metric while verification as demostratred below. ```python result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "VGG-Face", distance_metric = "cosine")